{"id":{"repo_id":"vcu","oai_identifier":"oai:scholarscompass.vcu.edu:etd-2365"},"canonical_url":"https://search.dev.ndltd.org/etd/vcu/oai:scholarscompass.vcu.edu:etd-2365","repository":{"repo_id":"vcu","name":"Virginia Commonwealth University","base_url":"https://scholarscompass.vcu.edu/do/oai/"},"display":{"title":"Membership Functions for a Fuzzy Relational Database: A Comparison of the Direct Rating and New Random Proportional Methods","abstract":"Fuzzy relational databases deal with imprecise data or fuzzy information in a relational database. The purpose of this fuzzy database implementation is to retrieve images by using fuzzy queries whose common-language descriptions are defined by the consensus of a particular user community. The fuzzy set, which is presentation of fuzzy attribute values of the images, is determined through membership function. This paper compares two methods of constructing membership functions, the Direct Rating and New Random Proportional, to determine which method gives maximum users satisfaction with minimum feedback from the community. The statistical analysis of results suggests the use of Direct Rating method. Moreover, the analysis shows that the performance of the New Random Proportional method can be improved with the inclusion of a \"Not\" modifier. This paper also identifies and analyzes issues that are raised by different versions of the database system.","abstract_html":"Fuzzy relational databases deal with imprecise data or fuzzy information in a relational database. The purpose of this fuzzy database implementation is to retrieve images by using fuzzy queries whose common-language descriptions are defined by the consensus of a particular user community. The fuzzy set, which is presentation of fuzzy attribute values of the images, is determined through membership function. This paper compares two methods of constructing membership functions, the Direct Rating and New Random Proportional, to determine which method gives maximum users satisfaction with minimum feedback from the community. The statistical analysis of results suggests the use of Direct Rating method. Moreover, the analysis shows that the performance of the New Random Proportional method can be improved with the inclusion of a "Not" modifier. This paper also identifies and analyzes issues that are raised by different versions of the database system.","abstract_has_math":false,"creators":["Sanghi, Shweta"],"institution":null,"degree_name":"Master of Science","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Dr. Lorraine M. Parker"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006-01-01T08:00:00Z","date_published":"2006-01-01T08:00:00Z","updated_at":"2026-07-24T05:55:14Z","subjects":["imprecise queries","membership function","fuzzy set theory","relational model","Data uncertainty","Fuzzy Database","Computer Sciences","Physical Sciences and Mathematics"],"languages":[],"rights":["© The Author"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarscompass.vcu.edu/etd/1366"],"render_values":[{"text":"https://scholarscompass.vcu.edu/etd/1366","href":"https://scholarscompass.vcu.edu/etd/1366","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.25772/QPMA-2B45","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Lorraine M. 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The purpose of this fuzzy database implementation is to retrieve images by using fuzzy queries whose common-language descriptions are defined by the consensus of a particular user community. The fuzzy set, which is presentation of fuzzy attribute values of the images, is determined through membership function. This paper compares two methods of constructing membership functions, the Direct Rating and New Random Proportional, to determine which method gives maximum users satisfaction with minimum feedback from the community. The statistical analysis of results suggests the use of Direct Rating method. Moreover, the analysis shows that the performance of the New Random Proportional method can be improved with the inclusion of a \"Not\" modifier. This paper also identifies and analyzes issues that are raised by different versions of the database system."]},{"key":"dc:title","label":"Title","values":["Membership Functions for a Fuzzy Relational Database: A Comparison of the Direct Rating and New Random Proportional Methods"]}]}],"canonical_facts":{"dc:contributor":["Dr. Lorraine M. Parker"],"dc:creator":["Sanghi, Shweta"],"dc:date.available":["2014-07-09T07:00:00Z"],"dc:description.abstract":["Fuzzy relational databases deal with imprecise data or fuzzy information in a relational database. The purpose of this fuzzy database implementation is to retrieve images by using fuzzy queries whose common-language descriptions are defined by the consensus of a particular user community. The fuzzy set, which is presentation of fuzzy attribute values of the images, is determined through membership function. This paper compares two methods of constructing membership functions, the Direct Rating and New Random Proportional, to determine which method gives maximum users satisfaction with minimum feedback from the community. The statistical analysis of results suggests the use of Direct Rating method. Moreover, the analysis shows that the performance of the New Random Proportional method can be improved with the inclusion of a \"Not\" modifier. This paper also identifies and analyzes issues that are raised by different versions of the database system."],"dc:identifier":["https://doi.org/10.25772/QPMA-2B45","https://scholarscompass.vcu.edu/etd/1366"],"dc:rights":["© The Author"],"dc:subject":["imprecise queries","membership function","fuzzy set theory","relational model","Data uncertainty","Fuzzy Database","Computer Sciences","Physical Sciences and Mathematics"],"dc:title":["Membership Functions for a Fuzzy Relational Database: A Comparison of the Direct Rating and New Random Proportional Methods"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T05:55:14Z"}